Research Engineer - Evals

Pantera Capital; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Benefits offered by this job

Competitive cash compensation

Equity opportunities

Relocation support

Immigration support

Job summary

Pantera Capital is seeking a dedicated professional to develop evaluation metrics for advanced AI models and consumer-grade agents in San Francisco. This role involves ensuring quality and trustworthiness in AI performance and shaping research outcomes by establishing rigorous evaluation standards.

You will advance evaluation frameworks across multiple dimensions and work closely with both researchers and product engineers.

Competitive cash and equity compensation packages are available, along with top-tier relocation support.

Qualifications

• Experience in measuring non-deterministic systems and understanding agent behavior.
• Ability to create evaluation metrics while avoiding gaming.

Responsibilities

• Build evaluation harnesses for various model capabilities.
• Develop dashboards for research and decision-making.
• Set the standards for product readiness.

Skills

Agent evaluation

Tool use

Non-deterministic systems measurement

Multilingual behavior

Job description

Location

San Francisco Office

Employment Type

Full time

Department

Engineering

Think Different. Build the Future.

Our Mission

Build everyday AGI. Trustworthy, consumer-grade agents that redefine human–AI collaboration for millions. Software shouldn’t wait for commands; it should partner with you, amplifying what you can do every single day.

Why AGI, Inc.

We’re a stealth team of elite founders and AI researchers, with backgrounds spanning Stanford, OpenAI, and DeepMind. We’re industry leaders in mobile and computer-use agents, bringing these capabilities to consumer scale.

Grounded in years of agent research, our AI is designed with trustworthiness and reliability as core pillars, not afterthoughts.

We are supported by tier-1 investors who funded the first generation of AI giants; now they’re backing us to build the next: everyday AGI. (Watch the demo)

If you see possibility where others see limits, read on.

You decide what "better" means.

Models, agents, and product features all ship behind one question: did this actually get better? Without a strong evals function, the lab ships vibes. With one, every training run, every prompt change, every agent capability moves a number we trust — and the team makes decisions on real signal, not the loudest opinion in the room.

You'll build the eval harness for AGI — across model capability, agentic behavior, on-device performance, and end-user experience. You'll set the bar for what counts as "shipped" and protect it from the gravity of product deadlines.

Tasks you will own

• The eval suites that gate every model and agent release — capability, behavior, regressions, and human-rated rubrics that catch what automated evals miss
• The dashboards and tooling that make researcher experiment loops fast and leadership decisions easy
• The bar — what counts as ready to ship, and how we know

Areas where you will assist

• Research, by making sure what we measure is what we want
• Product engineers, by instrumenting real-user behavior on real devices
• Partnerships, by translating "did it get better" into language an OEM partner can hold us to

Skills you'll be expected to teach

• How to measure non-deterministic systems — agent eval, tool use, long-horizon tasks, multilingual behavior
• How to push back on a metric that's being gamed without breaking the team

Skills you'll be expected to learn

• On-device perf trade-offs and how they show up in real-user evals
• What QA-ing AI at OEM scale actually looks like
• The realities of shipping consumer agents to production partners

Timeline of success

After 30 days — You've audited every eval we run today and produced a sharp doc on what's good, what's noise, and what's missing. You've fixed the most embarrassing gap.

After 60 days — You've stood up a new eval surface — agentic, on-device, or behavioral — and the team is making real decisions on its output. Researchers come to you before launching a run, not after.

After 90 days — Releases now ship against your eval bar, not a vibe-check. You've caught a regression that would have shipped, and cleared a launch the team was nervous about. You're shaping the research roadmap by surfacing where we're flat, where we're climbing, and where we're lying to ourselves.

Compensation

Competitive cash and meaningful equity. Top-tier relocation and immigration support. SF, in person.